Deep Variational Autoencoders for NPC Behaviour Classification
Everton S. Soares, Vadim Bulitko · 2019 IEEE Conference on Games (CoG) · 2019
Procedural content generation (PCG) can create novel, player-specific content in video games, including behaviours of AI-controlled non-playable characters (NPC). Here we present our first results on comparing unsupervised and supervised machine learning for procedurally generated NPC behaviours. Using an artificial life environment as a stand-in for a video game, we run artificial evolution and generate AI agents with various behaviours. We then train deep variational autoencoders on commonly evolved behaviour and measure its efficacy in detecting behaviours unseen during training. As a reference, we use an off-the-shelf deep network trained in a supervised manner to detect behaviours both seen and unseen during its training. Preliminary results demonstrate promising performance that holds even when the training set contains a mixture of several types of behaviours without proper labels.